There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R²) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm³/cm³. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R² and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

中文翻译：

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从综合多卫星数据中检索土壤水分含量的 GNSS-IR 方法，该方法考虑了植被水分含量的影响

使用全球导航卫星系统-干涉反射计（GNSS-IR）从单卫星数据反演土壤含水量（SMC）存在两个问题：反射区域的差异，以及难以规避季节性植被的影响反射微波信号的增长。本研究提出了一种基于综合多卫星数据对植被水分含量 (VMC) 影响的多元自适应回归样条 (MARS) SMC 反演模型。用多径效应计算的归一化微波反射指数（NMRI）映射到归一化差异植被指数（NDVI），以估计和消除VMC的影响。基于相移建立了从多卫星数据中提取SMC的MARS模型。为了检验它的可靠性，将MARS模型与多元线性回归（MLR）模型、反向传播神经网络（BPNN）模型和支持向量回归（SVR）模型在典型台站采集的时间序列观测数据的检索精度方面进行了比较. 本研究提出的MARS模型有效地反演了SMC，相关系数（R² ) 为 0.916，均方根误差 (RMSE) 为 0.021 cm ³ /cm ³。植被影响的消除导致 R ²增加 3.7%、13.9%、11.7% 和 16.6% ，MLR、BPNN 检索的 SMC 的 RMSE 分别降低 31.3%、79.7%、49.0% 和 90.5% 、SVR 和 MARS 模型。结果证明了基于多径效应修正植被变化的可行性以及MARS模型在反演SMC中的可靠性。